Change propagations in the maintenance of kernel-based software with a study on Linux

ABSTRACT

As a software system evolves to accommodate new features and repair bugs, changes are needed. Software components are interdependent, changes made to one component can require changes to be propagated to other components. Change propagation brings potential challenges for software maintenance. In this paper, we divide change propagations into four categories in kernel-based software. Different categories of change propagation have different effects on kernel maintenance. We use product version history to mine change propagations rules and apply the categorization to Linux operating system. Our study provides a framework for measuring, evaluating, and predicting change propagations in kernel-based software, which includes most operating systems, database management systems, game systems, and software product lines.

REFERENCES

• 1: Ahmed E. Hassan , Richard C. Holt, Predicting Change Propagation in Software Systems, Proceedings of the 20th IEEE International Conference on Software Maintenance, p.284-293, September 11-14, 2004
• 2: Per Brinch Hansen, The nucleus of a multiprogramming system, Communications of the ACM, v.13 n.4, p.238-241, April 1970 [doi>10.1145/362258.362278]
• 3: Theo Härder, New approaches to object processing in engineering databases, Proceedings on the 1986 international workshop on Object-oriented database systems, p.217, September 23-26, 1986, Pacific Grove, California, United States
• 4: Xbox365, 2006. Xbox system software overview.
• 5: Software product lines: practices and patterns, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 2001
• 6: Stevens, W. P., Myers, G. Z., and Constantine, L. L., "Structured Design", IBM Systems Journal, 1974, 13(2): 115--139.
• 7: Thomas Zimmermann , Stephan Diehl , Andreas Zeller, How History Justifies System Architecture (or Not), Proceedings of the 6th International Workshop on Principles of Software Evolution, p.73, September 01-02, 2003
• 8: Thomas Zimmermann , Peter Weisgerber , Stephan Diehl , Andreas Zeller, Mining Version Histories to Guide Software Changes, Proceedings of the 26th International Conference on Software Engineering, p.563-572, May 23-28, 2004
• 9: Thomas Zimmermann , Peter Weissgerber , Stephan Diehl , Andreas Zeller, Mining Version Histories to Guide Software Changes, IEEE Transactions on Software Engineering, v.31 n.6, p.429-445, June 2005 [doi>10.1109/TSE.2005.72]
• 10: Annie T. T. Ying , Gail C. Murphy , Raymond Ng , Mark C. Chu-Carroll, Predicting Source Code Changes by Mining Change History, IEEE Transactions on Software Engineering, v.30 n.9, p.574-586, September 2004 [doi>10.1109/TSE.2004.52]
• 11: Todd L. Graves , Alan F. Karr , J. S. Marron , Harvey Siy, Predicting Fault Incidence Using Software Change History, IEEE Transactions on Software Engineering, v.26 n.7, p.653-661, July 2000 [doi>10.1109/32.859533]
• 12: Chadd C. Williams , Jeffrey K. Hollingsworth, Automatic Mining of Source Code Repositories to Improve Bug Finding Techniques, IEEE Transactions on Software Engineering, v.31 n.6, p.466-480, June 2005 [doi>10.1109/TSE.2005.63]
• 13: Raymond, E. S., The Cathedral & the Bazaar, First Edition, O'Reilly, 2001.

INDEX TERMS

Primary Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.7 Distribution, Maintenance, and Enhancement

Additional Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
        D.2.10 Design**
      D.2.8 Metrics

General Terms:
Design

Keywords:
change propagation, data mining, maintenance